176 lines
7.1 KiB
Python
176 lines
7.1 KiB
Python
import re
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import uuid
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import math
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from pathlib import Path
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from typing import List, Dict, Any, Optional, Tuple
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import pandas as pd
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import chardet
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from openpyxl import load_workbook
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from app.config import MAX_HEADER_SCAN_ROWS, UPLOAD_DIR
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def clean_value(val):
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"""清理單一值，將 NaN/Inf 轉換為 None 以便 JSON 序列化"""
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if val is None:
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return None
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if isinstance(val, float):
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if math.isnan(val) or math.isinf(val):
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return None
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return val
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def clean_dict(d: Dict) -> Dict:
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"""清理字典中的所有 NaN/Inf 值"""
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return {k: clean_value(v) for k, v in d.items()}
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def clean_records(records: List[Dict]) -> List[Dict]:
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"""清理記錄列表中的所有 NaN/Inf 值"""
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return [clean_dict(r) for r in records]
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# 欄位名稱對應表
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COLUMN_MAPPING = {
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'dit': {
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'op_id': ['opportunity name', 'opportunity no', 'opportunity', 'op編號', 'op 編號', 'op_id', 'opid', '案件編號', '案號', 'opportunity id'],
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'erp_account': ['erp account', 'account no', 'erp account no', '客戶代碼', '客戶編號', 'erp_account'],
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'customer': ['account name', 'branding customer', '客戶', '客戶名稱', 'customer', 'customer name', '公司名稱'],
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'pn': ['product name', '料號', 'part number', 'pn', 'part no', 'part_number', '產品料號', 'stage/part'],
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'eau': ['eau quantity', 'eau quantity (pcs)', 'eau', '年預估量', 'annual usage', '預估用量'],
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'stage': ['stage', 'oppty product stage', '階段', 'status', '狀態', '專案階段'],
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'date': ['created date', '日期', 'date', '建立日期', 'create date']
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},
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'sample': {
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'sample_id': ['樣品訂單號碼', 'item', '樣品編號', 'sample_id', 'sample id', '編號'],
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'order_no': ['樣品訂單號碼', '單號', 'order_no', 'order no', '樣品單號', '申請單號'],
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'oppy_no': ['oppy no', 'oppy_no', '案號', '案件編號', 'opportunity no'],
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'cust_id': ['cust id', 'cust_id', '客戶編號', '客戶代碼', '客戶代號'],
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'customer': ['客戶名稱', '客戶簡稱', '客戶', 'customer', 'customer name'],
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'pn': ['item', 'type', '料號', 'part number', 'pn', 'part no', '產品料號', '索樣數量'],
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'qty': ['索樣數量pcs', '索樣數量 k', '數量', 'qty', 'quantity', '申請數量'],
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'date': ['需求日', '日期', 'date', '申請日期']
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},
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'order': {
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'order_id': ['項次', '訂單編號', 'order_id', 'order id'],
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'order_no': ['訂單單號', '訂單號', 'order_no', 'order no', '銷貨單號'],
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'cust_id': ['客戶編號', '客戶代碼', '客戶代號', 'cust_id', 'cust id'],
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'customer': ['客戶', '客戶名稱', 'customer', 'customer name'],
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'pn': ['type', '內部料號', '料號', 'part number', 'pn', 'part no', '產品料號'],
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'qty': ['訂單量', '數量', 'qty', 'quantity', '訂購數量', '出貨數量'],
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'status': ['狀態', 'status', '訂單狀態'],
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'amount': ['原幣金額(含稅)', '台幣金額(未稅)', '金額', 'amount', 'total', '訂單金額']
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}
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}
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class ExcelParser:
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def __init__(self):
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self.parsed_files: Dict[str, Dict] = {}
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def detect_encoding(self, file_path: Path) -> str:
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"""偵測檔案編碼"""
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with open(file_path, 'rb') as f:
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result = chardet.detect(f.read(10000))
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return result.get('encoding', 'utf-8')
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def find_header_row(self, df: pd.DataFrame, file_type: str) -> int:
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"""自動偵測表頭位置"""
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expected_columns = set()
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for variants in COLUMN_MAPPING[file_type].values():
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expected_columns.update([v.lower() for v in variants])
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for idx in range(min(MAX_HEADER_SCAN_ROWS, len(df))):
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row = df.iloc[idx]
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row_values = [str(v).lower().strip() for v in row.values if pd.notna(v)]
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# 檢查是否有匹配的欄位名稱
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matches = sum(1 for v in row_values if any(exp in v for exp in expected_columns))
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if matches >= 2: # 至少匹配 2 個欄位
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return idx
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return 0 # 預設第一行為表頭
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def map_columns(self, df: pd.DataFrame, file_type: str) -> Dict[str, str]:
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"""將 DataFrame 欄位對應到標準欄位名稱"""
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mapping = {}
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column_map = COLUMN_MAPPING[file_type]
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df_columns = [str(c).lower().strip() for c in df.columns]
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for standard_name, variants in column_map.items():
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for variant in variants:
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variant_lower = variant.lower()
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for idx, col in enumerate(df_columns):
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if variant_lower in col or col in variant_lower:
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mapping[df.columns[idx]] = standard_name
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break
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if standard_name in mapping.values():
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break
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return mapping
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def parse_file(self, file_path: Path, file_type: str) -> Tuple[str, Dict[str, Any]]:
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"""解析 Excel/CSV 檔案"""
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file_id = str(uuid.uuid4())
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# 讀取檔案
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if file_path.suffix.lower() == '.csv':
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encoding = self.detect_encoding(file_path)
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df = pd.read_csv(file_path, encoding=encoding, header=None)
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else:
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df = pd.read_excel(file_path, header=None)
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# 找到表頭
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header_row = self.find_header_row(df, file_type)
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# 重新讀取，以正確的表頭
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if file_path.suffix.lower() == '.csv':
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df = pd.read_csv(file_path, encoding=encoding, header=header_row)
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else:
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df = pd.read_excel(file_path, header=header_row)
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# 欄位對應
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column_mapping = self.map_columns(df, file_type)
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df = df.rename(columns=column_mapping)
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# 只保留需要的欄位
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required_columns = list(COLUMN_MAPPING[file_type].keys())
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available_columns = [c for c in required_columns if c in df.columns]
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df = df[available_columns]
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# 清理資料
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df = df.dropna(how='all')
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# 產生預覽資料（清理 NaN 值以便 JSON 序列化）
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preview = clean_records(df.head(10).to_dict(orient='records'))
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# 儲存解析結果
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parsed_data = {
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'file_id': file_id,
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'file_type': file_type,
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'filename': file_path.name,
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'header_row': header_row,
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'row_count': len(df),
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'columns': list(df.columns),
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'preview': preview,
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'dataframe': df
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}
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self.parsed_files[file_id] = parsed_data
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return file_id, {k: v for k, v in parsed_data.items() if k != 'dataframe'}
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def get_parsed_data(self, file_id: str) -> Optional[pd.DataFrame]:
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"""取得解析後的 DataFrame"""
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if file_id in self.parsed_files:
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return self.parsed_files[file_id].get('dataframe')
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return None
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def get_file_info(self, file_id: str) -> Optional[Dict]:
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"""取得檔案資訊"""
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if file_id in self.parsed_files:
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data = self.parsed_files[file_id]
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return {k: v for k, v in data.items() if k != 'dataframe'}
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return None
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# 全域實例
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excel_parser = ExcelParser()
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